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55 lines
1.5 KiB
Matlab
55 lines
1.5 KiB
Matlab
2 years ago
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imds=imageDatastore( ...
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'SignsCutted', ...
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'IncludeSubfolders',true, ...
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'LabelSource','foldernames');
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fprintf("Anzahl Bilder: %d\n", length(imds.Labels));
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rng(7);
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[imdsTrain, idmsValidation] = splitEachLabel(imds,0.7,'randomized');
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fprintf("Trainingsmenge Anzahl Elemente: %d Test(Validierungs)menge: %d\n", ...
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length(imdsTrain.Labels), length(idmsValidation.Labels));
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net=alexnet;
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layersTransfer = net.Layers(1:end-3);
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numClasses = numel(categories(imdsTrain.Labels));
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layers = [
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layersTransfer
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fullyConnectedLayer(numClasses, ...
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'WeightLearnRateFactor',20, ...
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'BiasLearnRateFactor',20)
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softmaxLayer
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classificationLayer
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];
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options = trainingOptions('sgdm', ...
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MiniBatchSize=10, ... % 10
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MaxEpochs=6, ... % 6
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InitialLearnRate=1e-4, ... % 1e-4
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ValidationData=idmsValidation, ...
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ValidationFrequency=3, ... % 3
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ValidationPatience=10, ... % 5
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Verbose=false, ... % false
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Plots='training-progress');
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netTransfer = trainNetwork(imdsTrain, layers, options);
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[yPred, scores] = classify(netTransfer, idmsValidation);
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idx=randperm(numel(idmsValidation.Files), 64);
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figure
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for i=1 : 64
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subplot(8,8,i)
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I = readimage(idmsValidation, idx(i));
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imshow(I)
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label = yPred(idx(i));
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title(string(label));
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end
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accuracy = mean(yPred == idmsValidation.Labels);
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fprintf("Genauigkeit: %3.2f\n", accuracy*100);
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